Field of the Invention
This disclosure relates to an image processing apparatus, image processing method, and image processing system.
Description of the Related Art
One important technique of image processing techniques is image segmentation. Image segmentation is a process of distinguishing a region of interest existing in an image from other regions. Many segmentation methods have been proposed, and a new segmentation method called a Graph-Cut method is recently attracting attention (U.S. Pat. No. 6,973,212). In this Graph-Cut method described in U.S. Pat. No. 6,973,212, an energy function is defined by a linear sum obtained by weighting two energy terms called a data term and smoothing term by a constant. A graph is formed in accordance with this energy function definition, and the formed graph is cut by using a minimum-cut/maximum-flow algorithm. Then, a region of interest in an image is acquired based on the cut graph.
Extraction of a plurality of regions of interest will be explained below. Generally, different regions of interest have different shapes and different dimensions. For example, when extracting a region of a pulmonary nodule from a chest CT image obtained by an X-ray CT apparatus, the shape and dimension of a pulmonary nodule change from one subject to another and from one portion to another in a pulmonary area. That is, even when the types (for example, pulmonary nodules) of regions of interest are the same, different regions of interest naturally have different shapes and different dimensions.
When regions of interest existing in an image have different shapes and different dimensions, the ratios of the regions of interest in the image and the ranges (the circumferential lengths of the regions of interest in a two-dimensional image, and the surface areas in a three-dimensional image) of boundaries between the regions of interest and peripheral regions are different. In the energy function of the Graph-Cut method, the value of the data term generally changes in proportion to the dimension of a region of interest. On the other hand, the value of the smoothing term generally changes in proportion to the square of the dimension of a region of interest. Also, even when the dimension of a region of interest remains the same, if the shape of the region of interest changes, the value of the smoothing term changes. In regions of interest different in shape and dimension, therefore, the ratio of the data term value to the smoothing term value changes.
When using a predetermined constant as a coefficient by which the data term or smoothing term is multiplied as described in U.S. Pat. No. 6,973,212, the ratio of the influence from the data term to the influence from the smoothing term largely changes from one region of interest to another. Accordingly, it may be impossible to properly extract a region of interest having a shape and dimension different from those when the constant is determined (that is, as expected when the constant is determined).
Japanese Patent Laid-Open No. 2013-156094 has disclosed a technique of extracting a region of interest having a desired dimension by the Graph-Cut method by designating an expected value of the dimension of the region by an operator. In this disclosed technique, however, an operator must designate an expected value of the dimension of each region of interest, and this increases the load on the operator.